Seismic full waveform inversion (FWI) is a widely used technique in geophysics for inferring subsurface structures from seismic data. And InversionNet is one of the most successful data-driven machine learning models that is applied to seismic FWI. However, the high computing costs to run InversionNet have made it challenging to be efficiently deployed on edge devices that are usually resource-constrained. Therefore, we propose to employ the structured pruning algorithm to get a lightweight version of InversionNet, which can make an efficient inference on edge devices. And we also made a prototype with Raspberry Pi to run the lightweight InversionNet. Experimental results show that the pruned InversionNet can achieve up to 98.2 % reduction in computing resources with moderate model performance degradation.

翻译：地震全波形反演（FWI）是地球物理学中用于从地震数据推断地下结构的一种广泛应用技术。其中，InversionNet是应用于地震FWI最成功的数据驱动机器学习模型之一。然而，运行InversionNet的高计算成本使其难以高效部署在资源通常受限的边缘设备上。因此，我们提出采用结构化剪枝算法获取轻量级版本的InversionNet，从而在边缘设备上实现高效推断。我们还利用树莓派制作了原型机来运行轻量化的InversionNet。实验结果表明，剪枝后的InversionNet在模型性能适度下降的情况下，计算资源可减少高达98.2%。